Vector-based Model Research Articles

The impact of heterogeneous accessibility to metro stations on land use changes in a bike-sharing context

The integration of urban rail transit and land use has been adopted as a crucial approach to fostering compact development in cities. Proximity to rail transit stations can increase the probability of land use changes, while few studies have analyzed the spatial heterogeneity of the impact of rail transit on land use changes. This study proposes a distance-decay function to delineate the spatial heterogeneity of metro station accessibility using bike-sharing data and examines the impact of metro stations accessibility on land use changes. Jiading New Town in Shanghai, China, is selected as our study case. By utilizing a non-linear distance-decay function to delineate metro station accessibility as a driving factor for training neural networks in a vector-based cellular automata model, an improvement in simulation accuracy is achieved compared to the model using a linear distance-decay function. This study could help establish more efficient strategies for promoting integrated development of rail transit and land use. The significance of this study lies in the generality of the optimized land use vector-based cellular automata model considering the spatial heterogeneity of metro station accessibility, which could also be applied to other situations, such as considering the accessibility to bus stops and road networks.

Expanded virtual vector-based model forecastable control strategy for three-phase two-level inverters

Abstract A three-phase, two-level inverter based on model forecastable control has good transient performance but poor steady-state performance. Considering the limitation of a limited number of discrete voltage vectors in a two-level inverter topology, this paper proposes a virtual vector model prediction method based on the analysis of a time-stepped forecasting model for a three-phase grid-connected inverter. First, three fixed virtual voltage vectors are constructed in each sector, and the action time of the required fundamental vector is found. Then, the cost function is constructed, and the optimum is selected among the virtual and fundamental voltage vectors in each sector. Finally, a hardware-in-the-loop experiment was carried out based on the PE-Expert4 experimental platform developed by Myway Co., LTD. The results show that the proposed virtual vector model forecastable control strategy can effectively improve the transient and steady-state performance of three-phase two-level inverters.

Applying Trust Patterns to Model Complex Trustworthiness in the Internet of Things

Key aspects of communities of the Internet of Things (IoT) smart objects presenting social aspects are represented by trust and reputation relationships between the objects. Several trustworthiness models have been presented in the literature in the context of multi-smart object community that could be adopted in the IoT scenario; however, most of these approaches represent the different dimensions of trust using scalar measures, then integrating these measures in a global trustworthiness value. In this paper, we discuss the limitation of this approach in the IoT context, highlighting the necessity of modeling complex trust relationships that cannot be captured by a vector-based model, and we propose a new trust model in which the trust perceived by an object with respect to another object is modeled by a directed, weighted graph whose vertices are trust dimensions and whose arcs represent relationships between trust dimensions. By using this new model, we provide the IoT community with the possibility of representing also situations in which an object does not know a trust dimension, e.g., reliability, but it is able to derive it from another one, e.g., honesty. The introduced model can represent any trust structure of the type illustrated above, in which several trust dimensions are mutually dependent.

Adaptive Multi-Sensor Joint Tracking Algorithm with Unknown Noise Characteristics.

In this study, to solve the low accuracy of multi-space-based sensor joint tracking in the presence of unknown noise characteristics, an adaptive multi-sensor joint tracking algorithm (AMSJTA) is proposed. First, the coordinate transformation from the target object to the optical sensors is considered, and the observation vector-based measurement model is established. Then, the measurement noise characteristics are assumed to be white Gaussian noise, and the measurement covariance matrix is set as a constant. On this premise, the traditional iterative extended Kalman filter is applied to solve this problem. However, in most actual engineering applications, the measurement noise characteristics are unknown. Thus, a forgetting factor is introduced to adaptively estimate the unknown measurement noise characteristics, and the AMSJTA is designed to improve the tracking accuracy. Furthermore, the lower bound of the proposed algorithm is theoretically proved. Finally, numerical simulations are executed to verify the effectiveness and superiority of the proposed AMSJTA.

High-performance computing in healthcare:an automatic literature analysis perspective

The adoption of high-performance computing (HPC) in healthcare has gained significant attention in recent years, driving advancements in medical research and clinical practice. Exploring the literature on HPC implementation in healthcare is valuable for decision-makers as it provides insights into potential areas for further investigation and investment. However, manually analyzing the vast number of scholarly articles is a challenging and time-consuming task. Fortunately, topic modeling techniques offer the capacity to process extensive volumes of scientific literature, identifying key trends within the field. This paper presents an automatic literature analysis framework based on a state-of-art vector-based topic modeling algorithm with multiple embedding techniques, unveiling the research trends surrounding HPC utilization in healthcare. The proposed pipeline consists of four phases: paper extraction, data preprocessing, topic modeling and outlier detection, followed by visualization. It enables the automatic extraction of meaningful topics, exploration of their interrelationships, and identification of emerging research directions in an intuitive manner. The findings highlight the transition of HPC adoption in healthcare from traditional numerical simulation and surgical visualization to emerging topics such as drug discovery, AI-driven medical image analysis, and genomic analysis, as well as correlations and interdisciplinary connections among application domains.

A log-based non-convex relaxation regularized regression for robust face recognition

Regression methods are widely employed in face recognition applications. The vector-based norm regression model only represents the pixel by pixel correlation and exhibits poor performance with contiguous occlusion. In contrast, methods based on the nuclear norm describe the low-rank structural information, but it is difficult to achieve satisfactory performance when dealing with complex noise. To address this, a log-based non-convex relaxation regularized regression (log-NCRR) model for robust face recognition is proposed in this paper. We adopt the log-based matrix loss without additional parameters to characterize the low-rank part of error image. Considering sparsity, we design a log-based vector loss to describe the sparse part of the error matrix, which achieves a balance between the l0-norm and l1-norm. Additionally, a weighted non-convex relaxation of l2,1-norm is proposed to ensure the group sparsity of the regression coefficient. The optimization problem is optimized by ADMM, with the corresponding sub-problems easily solved by the generalized singular value shrinkage operator. Finally, experimental results on four public databases for various types of noise validate the effectiveness and robustness of log-NCRR in face recognition.

Measurement Modeling and Performance Analysis of a Bionic Polarimetric Imaging Navigation Sensor Using Rayleigh Scattering to Generate Scattered Sunlight.

The bionic polarimetric imaging navigation sensor (BPINS) is a navigation sensor that provides absolute heading, and it is of practical engineering significance to model the measurement error of BPINS. The existing BPINSs are still modeled using photodiode-based measurements rather than imaging measurements and are not modeled systematically enough. This paper proposes a measurement performance analysis method of BPINS that takes into account the geometric and polarization errors of the optical system. Firstly, the key error factors affecting the overall measurement performance of BPINS are investigated, and the Stokes vector-based measurement error model of BPINS is introduced. Secondly, based on its measurement error model, the effect of the error source on the measurement performance of BPINS is quantitatively analyzed using Rayleigh scattering to generate scattered sunlight as a known incident light source. The numerical results show that in angle of E-vector (AoE) measurement, the coordinate deviation of the principal point has a greater impact, followed by grayscale response inconsistency of CMOS and integration angle error of micro-polarization array, and finally lens attenuation; in degree of linear polarization (DoLP) measurement, the grayscale response inconsistency of CMOS has a more significant impact. This finding can accurately guide the subsequent calibration of BPINS, and the quantitative results provide an important theoretical reference for its optimal design.

Design and calibration of the 6-DOF motion tracking system integrated on the Stewart parallel manipulator.

Accurate pose measurement is crucial for parallel manipulators (PM). This study designs a novel integrated 6-DOF motion tracking system to achieve precise online pose measurement. However, the presence of geometric errors introduces imperfections in the accuracy of the measured pose. Based on the displacement information of six grating rulers, measurement pose is obtained through forward kinematics. By comparing the measurement results with the actual pose information captured by stereo vision, measurement errors can be obtained. A closed-loop vector-based kinematic model and an error model are established, and then the geometric errors are identified with the least-squares method. Finally, the geometric calibration experiments are conducted, and the results show that the measurement accuracy has significantly improved, with the average position error decreasing from 3.148 mm to 0.036 mm, and the average orientation error is decreased from 0.225° to 0.022°.

Multiple potential drop measurement – A new method for crack detection and crack length measurement

The Direct Current Potential Drop Method (DCPDM) is widely used for crack detection and crack length measurement in fatigue experiments. Unfortunately, crack length measurements are only possible when a calibration for a specified crack initiation site and potential probe location is available. Investigations by Hartweg and Bär (2019) as well as Nahbein and Bär (2022) on notched round specimens equipped with three potential probes placed in a distance of 120° on the circumference have shown that the crack initiation site can be determined using a simple vector-based model. In this model the normal vector of a plane span by the three potentials is calculated in cylindrical coordinates. In the present work a more detailed investigation of crack initiation and propagation is undertaken to show the possibilities offered by this method. During fatigue tests with the simultaneous measurement of the three potential drops using amplifiers of the control electronics, overloads were introduced in fixed intervals to mark the crack front on the fracture surface. The measurement of both crack depth and fractured area allows a calibration of the potential drop measurement. The calibration was performed on the radius coordinate n of the normal vector using a function suggested by Tiedemann (2016). The multiple potential drop measurement enables an early detection of crack initiation as well as a location of the crack initiation site on the circumference of the specimen. Using the Tiedemann function fitted to the measured data, it is possible to determine the crack length independent of the crack initiation site on the circumference of the specimen.

Authoring and Simulating Meandering Rivers

We present a method for interactively authoring and simulating meandering river networks. Starting from a terrain with an initial low-resolution network encoded as a directed graph, we simulate the evolution of the path of the different river channels using a physically-based migration equation augmented with control terms. The curvature-based terms in the equation allow us to reproduce phenomena identified in geomorphology, such as downstream migration of bends. Control terms account for the influence of the landscape topography and user-defined river trajectory constraints. Our model implements abrupt events that shape meandering networks, such as cutoffs forming oxbow lakes and avulsions. We visually show the effectiveness of our method and compare the generated networks quantitatively to river data by analyzing sinuosity and wavelength metrics. Our vector-based model runs at interactive rates, allowing for efficient authoring of large-scale meandering networks.

Fault Diagnosis Method for Railway Turnout with Pinball Loss-Based Multiclass Support Matrix Machine

The intelligent maintenance of railway equipment plays a pivotal role in advancing the sustainability of transportation and manufacturing. Railway turnouts, being an essential component of railway infrastructure, often encounter various faults, which present operational challenges. Existing fault diagnosis methods for railway turnouts primarily utilize vectorized monitoring data, interpreted either through vector-based models or distance-based measurements. However, these methods exhibit limited interpretability or are heavily reliant on standard curves, which impairs their performance or restricts their generalizability. To address these limitations, a railway turnouts fault diagnosis method with monitoring signal images and support matrix machine is proposed herein. In addition, a pinball loss-based multiclass support matrix machine (PL-MSMM) is designed to address the noise sensitivity limitations of the multiclass support matrix machine (MSMM). First, the time-series monitoring signals in one dimension are transformed into images in two dimensions. Subsequently, the image-based feature matrix is constructed. Then, the PL-MSMM model is trained using the feature matrix to facilitate the fault diagnosis. The proposed method is evaluated using a real-world operational current dataset, achieving a fault identification accuracy rate of 98.67%. This method outperforms the existing method in terms of accuracy, precision, and F1-score, demonstrating its superiority.

Revolutionizing Education: Advanced Machine Learning Techniques for Precision Recommendation of Top-Quality Instructional Materials

AbstractThe integration of educational technology in the modern classroom has transformed the way students learn yet challenges in providing high-quality materials persist. To address this, we propose a novel support vector-based long short-term memory (LSTM) recommendation model. Our model combines support vector machines (SVM) and LSTM networks to enhance accuracy. The SVM analyzes material content, identifying key features for topic relevance. Meanwhile, the LSTM assesses word sequences to predict material relevance to the topic. We conducted experiments on a diverse instructional dataset, demonstrating superior performance in accuracy and relevance compared to existing models. Our model adapts to new data and continuously improves based on user feedback. Therefore, our Support Vector-based LSTM recommendation model can revolutionize instructional material recommendations. Its accuracy and relevance enhance student engagement and learning outcomes, optimizing the educational experience.

Hydrological model-based streamflow reconstruction for Indian sub-continental river basins, 1951–2021

Streamflow is a vital component of the global water cycle. Long-term streamflow observations are required for water resources planning and management, hydroclimatic extremes analysis, and ecological assessment. However, long-term streamflow observations for the Indian-Subcontinental (ISC) river basins are lacking. Using meteorological observations, state-of-the-art hydrological model, and river routing model, we developed hydrological model-simulated monthly streamflow from 1951–2021 for the ISC river basins. We used high-resolution vector-based routing model (mizuRoute) to generate streamflow at 9579 stream reaches in the sub-continental river basins. The model-simulated streamflow showed good performance against the observed flow with coefficient of determination (R2) and Nash-Sutcliffe efficiency (NSE) above 0.70 for more than 60% of the gauge stations. The dataset was used to examine the variability in low, average, and high flow across the streams. Long-term changes in streamflow showed a significant decline in flow in the Ganga basin while an increase in the semi-arid western India and Indus basin. Long-term streamflow can be used for planning water management and climate change adaptation in the Indian sub-continent.

An Enhanced Virtual Vector-Based Model Predictive Control for PMSM Drives to Reduce Common-Mode Voltage Considering Dead Time Effect

An Enhanced Virtual Vector-Based Model Predictive Control for PMSM Drives to Reduce Common-Mode Voltage Considering Dead Time Effect

TTAF: A two-tier task assignment framework for cooperative unit-based crowdsourcing systems

Traditional task assignment follows a direct recruitment model in which requesters recruit and select workers to complete tasks. Because of the unclear division of roles and the diversity of each role’s mission, this model is neither efficient nor scalable. This paper introduces the concept of cooperative unit (CU), in which workers are organized into cooperative units, whose proxies bid for tasks from requesters based on worker characteristics. However, because of the decentralization of task assignment, quality control is complicated, and the benefits of different roles must be balanced. As a result, we propose a novel two-tier task assignment framework (TTAF) that produces high-quality results while striking the appropriate balance between requesters, CUs, and workers. We first propose a vector-based expertise representation model that evaluates workers’ expertise based on previous answers. Then, we devise a higher-tier task assignment between tasks and CUs that maximizes answer quality while staying within budget. The quality of the answers is ensured by aspects such as keyword coverage, overall expertise, and the number of workers. We also devise lower-tier task assignment, which evenly distributes tasks among workers such that more workers have the opportunity to perform tasks. The extensive evaluation shows that our proposed approaches achieve promising results.

Adaptive weighted least squares (AWLS): A new vector-based model to improve urban population estimation at small-area scale using morphology and attractiveness criteria

Fine-resolution urban population mapping is vital for many applications, including urban planning and disaster management. However, these data are rarely available. Despite the well-established correlation between urban population distribution and the physical parameters of residential areas and urban mobility, there needs to be a comprehensive model that effectively utilizes these relationships. This article proposed a novel model for population estimation, adaptive weighted least squares (AWLS), based on the correlations between urban morphology (e.g., physical parameters and shape of residential areas) and attractiveness (e.g., points of interest) and weighing them in the least square regression. This hierarchical model first uses the AWLS method to account for local correlations. Then, it disaggregates the population into lower-level spatial units specifically city blocks and parcels. The efficacy of the method is demonstrated in three neighborhoods in Tehran, Iran, with differences and similarities in terms of morphology and attractiveness. This method significantly improved population estimation accuracy, outperforming common global and local estimation models (11% and 8% in Neighborhood A, 14% and 13% in Neighborhood B, and 5% and 5% in Neighborhood C). This model successfully disaggregated the census tract (CT) population into city block and parcel levels, providing valuable data for crisis management.

Multi-proximity based embedding scheme for learning vector quantization-based classification of biochemical structured data

In this paper, we propose a data embedding technique for structured data that allows for the direct application of standard vector-based machine learning models without the need for explicit feature extraction. Our approach relies on multiple notions of data proximity, making it suitable for handling mixed data types or incorporating domain knowledge. Our method also reduces the computational costs of pairwise proximity calculations, resulting in improved efficiency and scalability. We demonstrate the effectiveness of our technique on graph and sequence datasets from the biochemical domain. In particular, we show that the method can efficiently be combined with interpretable machine learning approaches like relevance-based learning vector quantization for sophisticated classification learning.

Transformer Architecture-Based Transfer Learning for Politeness Prediction in Conversation

Politeness is an essential part of a conversation. Like verbal communication, politeness in textual conversation and social media posts is also stimulating. Therefore, the automatic detection of politeness is a significant and relevant problem. The existing literature generally employs classical machine learning-based models like naive Bayes and Support Vector-based trained models for politeness prediction. This paper exploits the state-of-the-art (SOTA) transformer architecture and transfer learning for respectability prediction. The proposed model employs the strengths of context-incorporating large language models, a feed-forward neural network, and an attention mechanism for representation learning of natural language requests. The trained representation is further classified using a softmax function into polite, impolite, and neutral classes. We evaluate the presented model employing two SOTA pre-trained large language models on two benchmark datasets. Our model outperformed the two SOTA and six baseline models, including two domain-specific transformer-based models using both the BERT and RoBERTa language models. The ablation investigation shows that the exclusion of the feed-forward layer displays the highest impact on the presented model. The analysis reveals the batch size and optimization algorithms as effective parameters affecting the model performance.

Space vector-based model predictive current controller for grid-connected converter under unbalanced and distorted grid without a phase-locked loop

This paper presents an improved current control strategy for a three-phase voltage source converter connected to distorted grid. The proposed controller can successfully compensate the induced harmonic currents, while suppressing possible active power ripple. To manage various objectives and deal with tough restrictions, model predictive control can offer a promising solution. However, a modified extended complex Kalman filter is introduced to estimate the positive and negative fundamental components of the grid voltages which are used with the real and reactive power references to generate pure sinusoidal reference currents without the need for a phase-locked loop (PLL), avoiding its challenges under abnormal grid conditions. An improved space vector-based model predictive control (SVM-MPC) is proposed to regulate the real and reactive power components. The main target of this controller is to maintain harmonic free grid currents, even in unbalanced and distorted grid conditions, with enhanced performance in terms of fast dynamic response and good steady-state behavior. The proposed SVM MPC uses Pythagoras theorem to determine duty factors, avoiding trigonometric functions, which improves the computational burden and implementation complexity. In addition, fixed switching frequency can be ensured. Simulation and experimental results are carried out to validate the effectiveness and the practical feasibility of the proposed controller under distorted grid conditions.

A Simplified Virtual-Vector-Based Model Predictive Control Technique With a Control Factor for Three-Phase SPMSM Drives

Compared with the single vector-based model predictive control (MPC) technique, the multiple vector-based MPC technique gains interests in improving the total harmonic distortion (THD) of the stator currents and reducing electromagnetic torque and stator flux ripples for a three-phase surface permanent magnet synchronous motor (SPMSM). However, the complicated enumeration and operation in vector selection and duty cycle calculation increase the computational burden and limit its peneration. Hence, in this paper, a simplified virtual vector-based MPC with a control factor for a three-phase SPMSM is proposed. Without sector identification and enumeration operations, the proposed technique utilizes the control factor to automatically generate a large number of virtual vectors to achieve equivalent performance effectiveness with multiple vector-based MPC technique with lower sampling frequency. Subsequently, regardless of the number of virtual vectors, the candidate virtual vectors are directly identified in a <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$x-y$</tex-math></inline-formula> coordinate system to greatly reduce the computational burden. In addition, the duration of the selected optimal virtual vector and the resultant switching sequences are easily obtained and arranged to even the switching losses among the switching devices and further optimize the THD of the stator current. The proposed technique is simplified and flexible. Its control effectiveness is verified by experimental results.